five

Data Sheet 1_Multimodal neuroimaging of Col4a1-mutant mouse models of Gould syndrome.docx

收藏
NIAID Data Ecosystem2026-05-10 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Multimodal_neuroimaging_of_Col4a1-mutant_mouse_models_of_Gould_syndrome_docx/30596462
下载链接
链接失效反馈
官方服务:
资源简介:
IntroductionCerebral small vessel disease (cSVD) is a leading cause of stroke and vascular contributions to cognitive impairment and dementia (VCID). Studying monogenic forms of cSVD can elucidate molecular pathways that are dysfunctional in the common sporadic forms and may serve as potential therapeutic targets. Mutations in COL4A1 and COL4A2 cause highly penetrant cSVD as part of the multisystem disorder known as Gould syndrome, which includes cerebrovascular manifestations such as porencephaly, early-onset stroke, leukoencephalopathy, and intracerebral hemorrhage (ICH). MethodsTo investigate how allelic heterogeneity influences cerebrovascular phenotypes, we examined five Col4a1 mutant mouse strains that collectively model the clinical spectrum of Gould syndrome. Each strain underwent multimodal magnetic resonance imaging (MRI) at 14.1 Tesla to assess radiological features characteristic of cSVD. ResultsMultimodal MRI successfully identified typical cSVD-associated lesions across all Col4a1 mutant strains. The imaging revealed heterogeneous expressivity among the allelic variants in terms of lesion prevalence, size, and number. Furthermore, analysis across strains identified brain regions that were consistently more vulnerable to cSVD-related lesions. DiscussionThese findings demonstrate that high-field multimodal MRI can sensitively detect and differentiate cerebrovascular abnormalities among Col4a1 mutant mouse models of Gould syndrome. The approach provides a powerful, noninvasive platform for assessing genotype–phenotype relationships and for identifying brain regions at heightened risk in cSVD, supporting its potential use in early diagnosis and mechanistic studies of vascular pathology.
创建时间:
2025-11-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作